SoroushVahidi/scidocs huggingface dataset:
Why does the microbiome affect behaviour?
(2018)

Growing evidence indicates that the mammalian microbiome can affect behaviour, and several symbionts even produce neurotransmitters. One common explanation for these observations is that symbionts have evolved to manipulate host behaviour for their benefit. Here, we evaluate the manipulation hypothesis by applying evolutionary theory to recent work on the gut–brain axis. Although the theory predicts manipulation by symbionts under certain conditions, these appear rarely satisfied by the genetically diverse communities of the mammalian microbiome. Specifically, any symbiont investing its resources to manipulate host behaviour is expected to be outcompeted within the microbiome by strains that do not manipulate and redirect their resources into growth and survival. Moreover, current data provide no clear evidence for manipulation. Instead, we show how behavioural effects can readily arise as a by-product of natural selection on microorganisms to grow within the host and natural selection on hosts to depend upon their symbionts. We argue that understanding why the microbiome influences behaviour requires a focus on microbial ecology and local effects within the host. The microbiota can influence host behaviour through the gut–brain axis. In this Opinion, Johnson and Foster explore the evolution of this relationship and propose that adaptations of competing gut microorganisms may affect behaviour as a by‑product, leading to host dependence.
Monolingual and Cross-Lingual Information Retrieval Models Based on (Bilingual) Word Embeddings
(2015)

We propose a new unified framework for monolingual (MoIR) and cross-lingual information retrieval (CLIR) which relies on the induction of dense real-valued word vectors known as word embeddings (WE) from comparable data. To this end, we make several important contributions: (1) We present a novel word representation learning model called Bilingual Word Embeddings Skip-Gram (BWESG) which is the first model able to learn bilingual word embeddings solely on the basis of document-aligned comparable data; (2) We demonstrate a simple yet effective approach to building document embeddings from single word embeddings by utilizing models from compositional distributional semantics. BWESG induces a shared cross-lingual embedding vector space in which both words, queries, and documents may be presented as dense real-valued vectors; (3) We build novel ad-hoc MoIR and CLIR models which rely on the induced word and document embeddings and the shared cross-lingual embedding space; (4) Experiments for English and Dutch MoIR, as well as for English-to-Dutch and Dutch-to-English CLIR using benchmarking CLEF 2001-2003 collections and queries demonstrate the utility of our WE-based MoIR and CLIR models. The best results on the CLEF collections are obtained by the combination of the WE-based approach and a unigram language model. We also report on significant improvements in ad-hoc IR tasks of our WE-based framework over the state-of-the-art framework for learning text representations from comparable data based on latent Dirichlet allocation (LDA).
Easily Add Significance Testing to your Market Basket Analysis in SAS ® Enterprise Miner TM
(2012)

Market Basket Analysis is a popular data mining tool that can be used to search through data to find patterns of co-occurrence among objects. It is an algorithmic process that generates business rules and several metrics for each business rule such as support, confidence and lift that help researchers identify “interesting” patterns. Although useful, these popular metrics do not incorporate traditional significance testing. This paper describes how to easily add a well-known statistical significance test, the Pearson’s Chi Squared statistic, to the existing output generated by SAS® Enterprise Miner’s Association Node. The addition of this significance test enhances the ability of data analysts to make better decisions about which business rules are likely to be more useful.
Leveraging social media networks for classification
(2010)

Social media has reshaped the way in which people interact with each other. The rapid development of participatory web and social networking sites like YouTube, Twitter, and Facebook, also brings about many data mining opportunities and novel challenges. In particular, we focus on classification tasks with user interaction information in a social network. Networks in social media are heterogeneous, consisting of various relations. Since the relation-type information may not be available in social media, most existing approaches treat these inhomogeneous connections homogeneously, leading to an unsatisfactory classification performance. In order to handle the network heterogeneity, we propose the concept of social dimension to represent actors’ latent affiliations, and develop a classification framework based on that. The proposed framework, SocioDim, first extracts social dimensions based on the network structure to accurately capture prominent interaction patterns between actors, then learns a discriminative classifier to select relevant social dimensions. SocioDim, by differentiating different types of network connections, outperforms existing representative methods of classification in social media, and offers a simple yet effective approach to integrating two types of seemingly orthogonal information: the network of actors and their attributes.
From Word To Sense Embeddings: A Survey on Vector Representations of Meaning
(2018)

Over the past years, distributed semantic representations have proved to be effective and flexible keepers of prior knowledge to be integrated into downstream applications. This survey focuses on the representation of meaning. We start from the theoretical background behind word vector space models and highlight one of their major limitations: the meaning conflation deficiency, which arises from representing a word with all its possible meanings as a single vector. Then, we explain how this deficiency can be addressed through a transition from the word level to the more fine-grained level of word senses (in its broader acceptation) as a method for modelling unambiguous lexical meaning. We present a comprehensive overview of the wide range of techniques in the two main branches of sense representation, i.e., unsupervised and knowledge-based. Finally, this survey covers the main evaluation procedures and applications for this type of representation, and provides an analysis of four of its important aspects: interpretability, sense granularity, adaptability to different domains and compositionality.
Demographic Attributes Prediction on the Real-World Mobile Data
(2012)

The deluge of the data generated by mobile phone devices imposes new challenges on the data mining community. User activities recorded by mobile phones could be useful for uncovering behavioral patterns. An interesting question is whether patterns in mobile phone usage can reveal demographic characteristics of the user? Demographic information about gender, age, marital status, job type, etc. is a key for applications with customer centric strategies. In this paper, we describe our approach to feature extraction from raw data and building predictive models for the task of demographic attributes predictions. We experimented with graph based representation of users inferred from similarity of their feature vectors, feature selections and classifications algorithms. Our work contributes to the Nokia Mobile Data Challenge (MDC) in the endeavor of exploring the real-world mobile data.
The Effects of Profile Pictures and Friends' Comments on Social Network Site Users' Body Image and Adherence to the Norm
(2016)

This study sought to explore the effects of exposure to Facebook body ideal profile pictures and norm conforming comments on users' body image. In addition, the social identity and self-categorization theoretical frameworks were used to explore users' endorsement of a body ideal norm. A mock Facebook page was used to conduct a pretest posttest 2 × 2 between-group web-based experiment that featured body ideal profile pictures (body ideal vs. no body) and body ideal comments (conforming vs. nonconforming). Five hundred and one participants completed the experiment and passed all manipulation checks. Participants viewed pictures and comments on the status page and were able to leave their own comment before exiting. Results demonstrated no significant main effects. However, predispositional body satisfaction significantly moderated the relationship between body ideal pictures and body satisfaction. Most comments supported the body ideal norm. However, in support of self-categorization theory, participants exposed to nonconforming comments made nonconforming comments themselves significantly more than those exposed to conforming comments. The findings demonstrated the importance of continued body image research in social network sites, as well as the potential for self-categorization theory to guide such research.
(Invited) Nanoimprinted perovskite for optoelectronics
(2017)

Organic-inorganic hybrid perovskites have recently emerged as promising materials for optoelectronics. Here we show successful patterning of hybrid perovskite into nanostructures with cost-effective nanoimprint technology. Photodetectors are fabricated on nanoimprinted perovskite with improved responsivity. Nanoimprinted perovskite metasurface forms with significantly enhanced photoluminescence. Lasing is expected on nanoimprinted perovskite with optimized cavity design and process.
A 0.7V Fully-on-Chip Pseudo-Digital LDO Regulator with 6.3μA Quiescent Current and 100mV Dropout Voltage in 0.18-μm CMOS
(2018)

This paper presents an NMOS pseudo-digital low-dropout (PD-LDO) regulator that supports low-voltage operation by eliminating the amplifier of an analog LDO. The proposed pseudo-digital control loop consists of a latched comparator, a 2X charge pump and a RC network. It detects the output voltage and provides a continuous gate control signal for the power transistor by charging and discharging the RC network. Fast transient response is achieved due to the source follower structure of the power NMOS, with a small output capacitor and small occupied chip area and without consuming large quiescent current. The proof-of-concept design of the proposed PD-LDO is implemented in a 0.18-J.1m CMOS process. The minimum supply voltage is 0.7 V, with a dropout voltage of 100 mV and a maximum load current of 100 mA. Using only 20 pF of on-chip output capacitor and 10 MHz comparator clock frequency, the undershoot is 106 mV with 90 mA load current step and 150 ns edge time. The quiescent current is only 6.3 μA and the active chip area is 0.08 mm2.
Heart rate variability and psychometric responses to overload and tapering in collegiate sprint-swimmers.
(2017)

OBJECTIVES
The purpose of this study was to evaluate cardiac-parasympathetic and psychometric responses to competition preparation in collegiate sprint-swimmers. Additionally, we aimed to determine the relationship between average vagal activity and its daily fluctuation during each training phase.


DESIGN
Observational.


METHODS
Ten Division-1 collegiate sprint-swimmers performed heart rate variability recordings (i.e., log transformed root mean square of successive RR intervals, lnRMSSD) and completed a brief wellness questionnaire with a smartphone application daily after waking. Mean values for psychometrics and lnRMSSD (lnRMSSDmean) as well as the coefficient of variation (lnRMSSDcv) were calculated from 1 week of baseline (BL) followed by 2 weeks of overload (OL) and 2 weeks of tapering (TP) leading up to a championship competition.


RESULTS
Competition preparation resulted in improved race times (p<0.01). Moderate decreases in lnRMSSDmean, and Large to Very Large increases in lnRMSSDcv, perceived fatigue and soreness were observed during the OL and returned to BL levels or peaked during TP (p<0.05). Inverse correlations between lnRMSSDmean and lnRMSSDcv were Very Large at BL and OL (p<0.05) but only Moderate at TP (p>0.05).


CONCLUSIONS
OL training is associated with a reduction and greater daily fluctuation in vagal activity compared with BL, concurrent with decrements in perceived fatigue and muscle soreness. These effects are reversed during TP where these values returned to baseline or peaked leading into successful competition. The strong inverse relationship between average vagal activity and its daily fluctuation weakened during TP.
基於對照表以及語言模型之簡繁字體轉換 (Chinese Characters Conversion System based on Lookup Table and Language Model) [In Chinese]
(2010)

The character sets used in China and Taiwan are both Chinese, but they are divided into simplified and traditional Chinese characters. There are large amount of information exchange between China and Taiwan through books and Internet. To provide readers a convenient reading environment, the character conversion between simplified and traditional Chinese is necessary. The conversion between simplified and traditional Chinese characters has two problems: one-to-many ambiguity and term usage problems. Since there are many traditional Chinese characters that have only one corresponding simplified character, when converting simplified Chinese into traditional Chinese, the system will face the one-to-many ambiguity. Also, there are many terms that have different usages between the two Chinese societies. This paper focus on designing an extensible conversion system, that can take the advantage of community knowledge by accumulating lookup tables through Wikipedia to tackle the term usage problem and can integrate language model to disambiguate the one-to-many ambiguity. The system can reduce the cost of proofreading of character conversion for books, e-books, or online publications. The extensible architecture makes it easy to improve the system with new training data.
Checklists for improving rigour in qualitative research: a case of the tail wagging the dog?
(2001)

Qualitative research methods are enjoying unprecedented popularity. Although checklists have undoubtedly contributed to the wider acceptance of such methods, these can be counterproductive if used prescriptively. The uncritical adoption of a range of “technical fixes” (such as purposive sampling, grounded theory, multiple coding, triangulation, and respondent validation) does not, in itself, confer rigour. In this article I discuss the limitations of these procedures and argue that there is no substitute for systematic and thorough application of the principles of qualitative research. Technical fixes will achieve little unless they are embedded in a broader understanding of the rationale and assumptions behind qualitative research.
Yet Another Visit to Paxos
(2010)

This paper presents a modular introduction to crash-tolerant and Byzantine-tolerant protocols for reaching consensus that use the method introduced by the Paxos algorithm of Lamport and by the viewstamped replication algorithm of Oki and Liskov. The consensus protocol runs a sequence of epoch abstractions as governed by an epoch-change abstraction. Implementations of epoch and epoch-change that tolerate crash faults yield the consensus algorithm in Paxos and in viewstamped replication. Implementations of epoch and epoch-change that tolerate Byzantine faults yield the consensus algorithm in the PBFT protocol of Castro and Liskov.
Internet inter-domain traffic
(2010)

In this paper, we examine changes in Internet inter-domain traffic demands and interconnection policies. We analyze more than 200 Exabytes of commercial Internet traffic over a two year period through the instrumentation of 110 large and geographically diverse cable operators, international transit backbones, regional networks and content providers. Our analysis shows significant changes in inter-AS traffic patterns and an evolution of provider peering strategies. Specifically, we find the majority of inter-domain traffic by volume now flows directly between large content providers, data center / CDNs and consumer networks. We also show significant changes in Internet application usage, including a global decline of P2P and a significant rise in video traffic. We conclude with estimates of the current size of the Internet by inter-domain traffic volume and rate of annualized inter-domain traffic growth.
Strategies and struggles with privacy in an online social networking community
(2008)

Online social networking communities such as Facebook and MySpace are extremely popular. These sites have changed how many people develop and maintain relationships through posting and sharing personal information. The amount and depth of these personal disclosures have raised concerns regarding online privacy. We expand upon previous research on users’ under-utilization of available privacy options by examining users’ current strategies for maintaining their privacy, and where those strategies fail, on the online social network site Facebook. Our results demonstrate the need for mechanisms that provide awareness of the privacy impact of users’ daily interactions.
Moving beyond untagging: photo privacy in a tagged world
(2010)

Photo tagging is a popular feature of many social network sites that allows users to annotate uploaded images with those who are in them, explicitly linking the photo to each person's profile. In this paper, we examine privacy concerns and mechanisms surrounding these tagged images. Using a focus group, we explored the needs and concerns of users, resulting in a set of design considerations for tagged photo privacy. We then designed a privacy enhancing mechanism based on our findings, and validated it using a mixed methods approach. Our results identify the social tensions that tagging generates, and the needs of privacy tools to address the social implications of photo privacy management.
Information revelation and privacy in online social networks
(2005)

Participation in social networking sites has dramatically increased in recent years. Services such as Friendster, Tribe, or the Facebook allow millions of individuals to create online profiles and share personal information with vast networks of friends - and, often, unknown numbers of strangers. In this paper we study patterns of information revelation in online social networks and their privacy implications. We analyze the online behavior of more than 4,000 Carnegie Mellon University students who have joined a popular social networking site catered to colleges. We evaluate the amount of information they disclose and study their usage of the site's privacy settings. We highlight potential attacks on various aspects of their privacy, and we show that only a minimal percentage of users changes the highly permeable privacy preferences.
Persona: an online social network with user-defined privacy
(2009)

Online social networks (OSNs) are immensely popular, with some claiming over 200 million users. Users share private content, such as personal information or photographs, using OSN applications. Users must trust the OSN service to protect personal information even as the OSN provider benefits from examining and sharing that information. We present Persona, an OSN where users dictate who may access their information. Persona hides user data with attribute-based encryption (ABE), allowing users to apply fine-grained policies over who may view their data. Persona provides an effective means of creating applications in which users, not the OSN, define policy over access to private data. We demonstrate new cryptographic mechanisms that enhance the general applicability of ABE. We show how Persona provides the functionality of existing online social networks with additional privacy benefits. We describe an implementation of Persona that replicates Facebook applications and show that Persona provides acceptable performance when browsing privacy-enhanced web pages, even on mobile devices.
Estimating the Customer-Level Demand for Electricity Under Real-Time Market Prices
(1997)

This paper presents estimates of the customer-level demand for electricity by industrial and commercial customers purchasing electricity according to the half-hourly energy prices from the England and Wales (E&W) electricity market. These customers also face the possibility of a demand charge on their electricity consumption during the three half-hour periods that are coincident with E&W system peaks. Although energy charges are largely known by 4:00 P.M. the day prior to consumption, a fraction of the energy charge and the identity of the half-hour periods when demand charges occur are only known with certainty ex post of consumption. Four years of data from a Regional Electricity Company (REC) in the United Kingdom is used to quantify the half-hourly customer-level demands under this real-time pricing program. The econometric model developed and estimated here quantifies the extent of intertemporal substitution in electricity consumption across pricing periods within the day due to changes in all of the components of the day-ahead E&W electricity prices, the level of the demand charge and the probability that a demand charge will be imposed. The results of this modeling framework can be used by retail companies supplying consumers purchasing electricity according to real-time market prices to construct demand-side bids into an electricity market open to competition. The paper closes with several examples of how this might be done.
Combined digital signature and digital watermark scheme for image authentication
(2001)

Conventional digital signature schemes for image authentication encode the signature in a file separate from the original image, thus require extra bandwidth to transmit it. Meantime, the watermark technique embeds some information in the host image. In this paper, a combined digital watermark and digital signature scheme for image authentication is proposed. The scheme extracts signature from the original image and embeds them back into the image as watermark, avoiding additional signature file. Since images are always compressed before transmission in the Internet, the proposed scheme is tested for compression tolerance and shows good robustness against JPEG coding. Furthermore, the scheme not only can verify the authenticity and the integrity of images, but also can locate the illegal modifications.
Listening to Chaotic Whispers: A Deep Learning Framework for News-oriented Stock Trend Prediction
(2018)

Stock trend prediction plays a critical role in seeking maximized profit from the stock investment. However, precise trend prediction is very difficult since the highly volatile and non-stationary nature of the stock market. Exploding information on the Internet together with the advancing development of natural language processing and text mining techniques have enabled investors to unveil market trends and volatility from online content. Unfortunately, the quality, trustworthiness, and comprehensiveness of online content related to stock market vary drastically, and a large portion consists of the low-quality news, comments, or even rumors. To address this challenge, we imitate the learning process of human beings facing such chaotic online news, driven by three principles: sequential content dependency, diverse influence, and effective and efficient learning. In this paper, to capture the first two principles, we designed a Hybrid Attention Networks(HAN) to predict the stock trend based on the sequence of recent related news. Moreover, we apply the self-paced learning mechanism to imitate the third principle. Extensive experiments on real-world stock market data demonstrate the effectiveness of our framework. A further simulation illustrates that a straightforward trading strategy based on our proposed framework can significantly increase the annualized return.